SoroushVahidi/scidocs huggingface dataset:
A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text
(2016)

The expansion of the World Wide Web and the increasing popularity of microblogging websites such as Twitter and Facebook has created massive stores of textual data that is short in length. Although traditional topic models have proven to work successfully on collections of long texts such as books and news articles, they tend to produce results that are less coherent when applied to short text, such as status messages and product reviews. Over the last few decades it has become of greater relevance to analyse short texts due to the realisation that such bodies of text could potentially hold useful information. Latent Dirichlet allocation (LDA) is one of the most popular topic models and it makes the generative assumption that each document contains multiple topics in varying proportions, which is a sensible assumption about long text. On the other hand, the Dirichlet multinomial mixture model (GSDMM), a seemingly less popular topic model, assumes that a document can only belong to a single topic, which seems to be a more appropriate assumption for short text. The objective of this paper is to investigate the hypothesis that GSDMM will outperform LDA on short text, using topic coherence and stability as performance measures.
A Four-Plate Compact Capacitive Coupler Design and LCL-Compensated Topology for Capacitive Power Transfer in Electric Vehicle Charging Application
(2016)

This paper proposes a four-plate compact capacitive coupler and its circuit model for large air-gap distance capacitive power transfer (CPT). The four plates are arranged vertically, instead of horizontally, to save space in the electric vehicle charging application. The two plates that are on the same side are placed close to each other to maintain a large coupling capacitance, and they are of different sizes to maintain the coupling between the primary and secondary sides. The circuit model of the coupler is presented, considering all six coupling capacitors. The LCL compensation topology is used to resonate with the coupler and provide high voltage on the plates to transfer high power. The circuit model of the coupler is simplified to design the parameters of the compensation circuit. Finite-element analysis is employed to simulate the coupling capacitance and design the dimensions of the coupler. The circuit performance is simulated in LTspice to design the specific parameter values. A prototype of the CPT system was designed and constructed with the proposed vertical plate structure. The prototype achieved an efficiency of 85.87% at 1.88-kW output power with a 150-mm air-gap distance.
Bundle Adjustment - A Modern Synthesis
(1999)

This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than restricting attention to traditional nonlinear least squares.
Design, modeling and control of a 5-DoF light-weight robot arm for aerial manipulation
(2015)

The design, modeling and control of a 5 degrees-of-freedom light-weight robot manipulator is presented in this paper. The proposed robot arm, named Prisma Ultra-Lightweight 5 ARm (PUL5AR), is employed to execute manipulation tasks equipped on board of a vertical take-off and landing unmanned aerial vehicle. The arm is compact and light-weight. Its mechanics is designed such that it can fold on itself during landing manoeuvres. Moreover, the design is conceived to constrain the center of gravity of the arm as close as possible to vehicle base, thus reducing the total inertia and static unbalancing of the system. Experimental tests have been carried out in order to validate the dynamic model, the communication library, the developed electronics, and the control schemes implemented for the designed robot arm.
A Unified Passivity Based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots
(2005)

In this paper we describe a general passivity based framework for the control of flexible joint robots. Herein the recent DLR results on torque-, position-, as well as impedance control of flexible joint robots are summarized, and the relations between the individual contributions are highlighted. It is shown that an inner torque feedback loop can be incorporated into a passivity based analysis by interpreting torque feedback in terms of shaping of the motor inertia. This result, which implicitly was already included in our earlier works on torqueand position control, can also be seized for the design of impedance controllers. For impedance control, furthermore, potential shaping is of special interest. It is shown how, based only on the motor angles, a potential function can be designed which simultaneously incorporates gravity compensation and a desired Cartesian stiffness relation for the link angles. All the presented controllers were experimentally evaluated on the DLR light-weight robots and proved their performance and robustness with respect to uncertain model parameters. Herein, an impact experiment is presented briefly, and an overview of several applications is given in which the controllers have been applied.
Modeling and Control of MM-UAV: Mobile Manipulating Unmanned Aerial Vehicle
(2013)

Compared to autonomous ground vehicles, UAVs (unmanned aerial vehicles) have significant mobility advantages and the potential to operate in otherwise unreachable locations. Micro UAVs still suffer from one major drawback: they do not have the necessary payload capabilities to support high performance arms. This paper, however, investigates the key challenges in controlling a mobile manipulating UAV using a commercially available aircraft and a light-weight prototype 3arm manipulator. Because of the overall instability of rotorcraft, we use a motion capture system to build an efficient autopilot. Our results indicate that we can accurately model and control our prototype system given significant disturbances when both moving the manipulators and interacting with the ground.
Construction of Cubic Structures with Quadrotor Teams
(2011)

We propose and investigate a system in which teams of quadrotor helicopters assemble 2.5-D structures from simple structural nodes and members equipped with magnets. The structures, called Special Cubic Structures (SCS), are a class of 2.5-D truss-like structures free of overhangs and holes. Grippers attached to the bottom of each quadrotor enable them to pick up, transport, and assemble the structural elements. The design of the nodes and members imposes constraints on assembly which are incorporated into the design of the algorithms used for assembly. We show that any SCS can be built using only the feasible assembly modes for individual structural elements and present simulation and experimental results with a team of quadrotors performing automated assembly. The paper includes a theoretical analysis of the SCS construction algorithm, the rationale for the design of the structural nodes, members and quadrotor gripper, a description of the quadrotor control methods for part pickup, transport and assembly, and an empirical analysis of system performance.
Impact of FinFET Technology Introduction in the 3T1D-DRAM Memory Cell
(2013)

In this paper, the 3T1D-DRAM cell based on FinFET devices is studied as an alternative to the bulk one. We observe an improvement in its behavior when IG and SG FinFETs are properly mixed, since together they provide a relevant increase in the memory circuit retention time. Moreover, our FinFET cell shows larger variability robustness, better performance at low supply voltage, and higher tolerance to elevated temperatures.
IoT security attacks using reverse engineering methods on WSN applications
(2016)

With the rapid technological advancements of sensors, Wireless Sensor Networks (WSNs) have become a popular technology for the Internet of Things (IoT). We investigated the security of WSNs in an environmental monitoring application with the goal to demonstrate the overall security. We implemented a Secure Temperature Monitoring System (STMS), which served as our WSN application. Our results revealed a security flaw found in the bootstrap loader (BSL) password used to protect MSP430 micro-controller units (MCUs). We demonstrated how the BSL password could be brute forced in a matter of days. Furthermore, we illustrate how an attacker can reverse engineer WSN applications to obtain critical security information such as encryption keys. We contribute a solution to patch the weak BSL password security flaw and improve the security of MSP430 MCU chips. The Secure-BSL patch we contribute allows the randomization of the BSL password. Our solution increases the brute force time to decades. The impractical brute force time enhances the security of the MSP430 and prevents future reverse engineering tactics. Our research serves as proof that the security of WSNs and the overall IoT technology is broken if we cannot protect these everyday objects at the physical layer.
Cybersecurity Through Secure Software Development
(2015)

Reports about serious vulnerabilities in critical IT components have triggered increased focus on cybersecurity worldwide. Among the man y initiatives to strengthen cybersecurity it is common to see the establishment and stre gthening of CERTs and other centers for cybersecurity. On the other hand , strengthening education in IT security and applying methods for secure systems d velopment are methods that receive much less attention. In this paper we expla in how the lack of focus on security in IT education programs worldwide is a signifi cant contributor to security vulnerabilities, and we propose an agile method for s ecure software design that requires team members to have received adequa te security education and training.
Identifying Important Places in People's Lives from Cellular Network Data
(2011)

People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these “important places” is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.
ExerLink: enabling pervasive social exergames with heterogeneous exercise devices
(2012)

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.
WNGrad: Learn the Learning Rate in Gradient Descent
(2018)

Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function such as smoothness or strong convexity constants are known, theoretical learning rate schedules can be applied. However, in practice, such parameters are not known, and the loss function of interest is not convex in any case. The recently proposed batch normalization reparametrization is widely adopted in most neural network architectures today because, among other advantages, it is robust to the choice of Lipschitz constant of the gradient in loss function, allowing one to set a large learning rate without worry. Inspired by batch normalization, we propose a general nonlinear update rule for the learning rate in batch and stochastic gradient descent so that the learning rate can be initialized at a high value, and is subsequently decreased according to gradient observations along the way. The proposed method is shown to achieve robustness to the relationship between the learning rate and the Lipschitz constant, and near-optimal convergence rates in both the batch and stochastic settings (O(1/T ) for smooth loss in the batch setting, and O(1/ √ T ) for convex loss in the stochastic setting). We also show through numerical evidence that such robustness of the proposed method extends to highly nonconvex and possibly non-smooth loss function in deep learning problems. Our analysis establishes some first theoretical understanding into the observed robustness for batch normalization and weight normalization.
Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition
(2015)

We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3% was obtained from the best single deep CNN, while the single-level committees yielded 58.3% and 60.5% with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6%, significantly higher than the SFEW baseline of 39.1%.
Communicating neuronal ensembles between neuromorphic chips
(1998)

I describe an interchip communication system that reads out pulse trains from a 64 64 array of neurons on one chip, and transmits them to corresponding locations in a 64 64 array of neurons on a second chip. It uses a random-access, time-multiplexed, asynchronous digital bus to transmit log2N -bit addresses that uniquely identify each of theN neurons in the sending population. A peak transmission rate of 2.5MSpikes/s is achieved by pipelining the operation of the channel. I discuss how the communication channel design is optimized for sporadic stimulus-triggered activity which causes some small subpopulation to re in synchrony, by adopting an arbitered, event-driven architecture. I derive the bandwidth required to transmit this neuronal ensemble, without temporal dispersion, in terms of the number of neurons, the probability that a neuron is part of the ensemble, and the degree of synchrony.
Intent Mining for the Good , Bad & Ugly Use of Social Web : Concepts , Methods , and Challenges
(2018)

The social web has empowered us to easily share information, express opinions, and engage in discussions on events around the world. While users of social media platforms often o↵er help and emotional support to others (the good), they also spam (the bad) and harass others as well as even manipulate others via fake news (the ugly). In order to both leverage the positive e↵ects and mitigate the negative e↵ects of using social media, intent mining provides a computational approach to proactively analyze social media data. This chapter introduces an intent taxonomy of social media usage with examples and describes methods and future challenges to mine the intentional uses of social media.
K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion
(2016)

A sign language recognition system is an attempt to bring the speech and the hearing impaired community closer to more regular and convenient forms of communication. Thus, this system requires to recognize the gestures from a sign language and convert them to a form easily understood by the hearing. The model that has been proposed in this paper recognizes static images of the signed alphabets in the Indian Sign Language. Unlike the alphabets in other sign languages like the American Sign Language and the Chinese Sign language, the ISL alphabet are both single-handed and double-handed. Hence, to make recognition easier the model first categorizes them as single-handed or double-handed. For both categories two kinds of features, namely HOG and SIFT, are extracted for a set of training images and are combined in a single matrix. After which, HOG and SIFT features for the input test image are combined with the HOG and SIFT feature matrices of the training set. Correlation is computed for these matrices and is fed to a K-Nearest Neighbor Classifier to obtain the resultant classification of the test image.
Decentralized Edge Clouds
(2013)

Cloud computing services are traditionally deployed on centralized computing infrastructures confined to a few data centers, while cloud applications run in a single data center. However, the cloud's centralized nature can be limiting in terms of performance and cost for applications where users, data, and computation are distributed. The authors present an overview of distributed clouds that might be better suited for such applications. They briefly describe the distributed cloud landscape and introduce Nebula, a highly decentralized cloud that uses volunteer edge resources. The authors provide insights into some of its key properties and design issues, and describe a distributed MapReduce application scenario to illustrate the benefits and trade-offs of using distributed and decentralized clouds for distributed data-intensive computing applications.
A 5-Gbps CMOS Burst-Mode CDR Circuit With an Analog Phase Interpolator for PONs
(2015)

This paper presents a 5-Gb/s low-power burst-mode clock and data recovery circuit based on analog phase interpolator for passive optical network applications. The proposed clock recovery unit consists of two double-edge triggered sample-and-holds (DTSHs) and a phase interpolator. The PI instantaneously locks the recovered clock to incoming burst-mode data by coefficients generated at the DT-SHs’ outputs. To reduce power dissipation in clock recovery unit, instead of two buffers, only one is utilized for the DT-SH. The proposed PI-based BM-CDR has been designed and simulated in 0.18-μm standard CMOS technology. The Results show that reduction in power dissipation of 40% for the clock recovery unit has been achieved. The proposed BM-CDR circuit retimes data at 5Gb/s for a 210-1 pseudo-random binary sequence within the first UI. The recovered data shows jitter at 14ps (pp). The circuit, including 1:2 data demux, draws 29mW power from a 1.8-V supply.
Shape from shading for the digitization of curved documents
(2006)

Document digitization is faster and more affordable using digital cameras than scanners. On the other hand, if we aim at extending the basic digital camera functionalities for such a purpose, post-processings will be of first importance, at least to improve the text legibility. In this paper, we address the specific problem of the virtual flattening of curved documents, as for example the pages of an opened book lying on its spine. In order to compute the document shape, we use the shape from shading technique and discuss why, in some cases, it is more suitable than other 3D single-view reconstruction techniques. We extend the seminal work by Wada et al. (Proceedings of the IAPR Workshop on machine vision and applications, Tokyo, Japan, pp. 591–594, 1992) and consecutive papers, reformulating the problem in terms of perspective shape from shading. Finally, we design a complete post-processing algorithm and test it on real images. Even if the documents are much curved, it is shown that the restored images are almost identical to scanned images of the flattened documents.
Direct Synthesis of Complex Loaded Chebyshev Filters in a Complex Filtering Network
(2016)

In this paper, a direct synthesis approach is proposed for designing a Chebyshev filter that matches the frequency variant complex loads at both the ports. The approach is based on the power wave renormalization theory and two practical assumptions: 1) the prescribed transmission zeros are stationary and 2) the reflection zeros are located along an imaginary axis. Three conditions are derived to stipulate the characteristic polynomials of the filter's responses through the renormalization of reference load impedances. These conditions are sequentially applied to ensure that the filter is physically realizable and well matched to the complex loads. It has been shown that the optimally synthesized filtering network that consists of an ideal filter cascaded with a piece of transmission line of an optimal length at each port can match the complex loads over a given frequency band with the best effort. By incorporating with an adjustment of the junction parameters, the approach provides an analytical yet flexible way to synthesize advanced microwave circuits composed of multiple filters connected together through some generalized junctions. The effectiveness of the method is demonstrated through three synthesis examples.
Analyze your Scratch projects with Dr . Scratch and assess your Computational Thinking skills
(2015)

In this paper we present the feature of Dr. Scratch that allows to automatically assessing the Computational Thinking skills of Scratch projects. The paper reviews similar initiatives, like Hairball, and investigates the literature with proposals for assessment of Scratch projects that we have studied and remixed in order to develop the Computational Thinking analysis. Then it introduces the various aspects that Dr. Scratch takes into consideration to compute a Computational Thinking score for a Scratch project and presents some preliminary findings of the analysis of over 100 investigated Scratch projects. Finally, future directions and limitations are presented and discussed.